Despite advances in magnetic resonance (MR) scanner hardware and imaging techniques, physiologic and involuntary patient motion remains a problem in many applications. Over the years, both prospective and retrospective techniques have been developed for compensating for artifacts resulting from the motion. Among these are physiologic gating, data reordering, and spatial presaturation. Although effective, these techniques are limited to known periodic physiologic movements, and cannot be used for arbitrary motion. One means of compensation for general motion is the method of “navigator echoes,” which requires the acquisition of additional projection data during the scan to extract the motion information. In certain fast or high-resolution imaging sequences, however, it may not be desirable or feasible to obtain additional data, since the minimum sequence repetition time (TR) or the total scan time could become prolonged. The use of navigator echoes may also undesirably affect the steady state.
Post-processing techniques also have been used for motion artifact compensation, either without or combined with additional navigator echoes. Some of the earlier methods were only applicable to translational motion, using edge detection to recover motion along read-out direction and an iterative procedure to remove the remaining phase error. These techniques depend on the existence of significant ghosting and sharp object boundaries, which are not always present in in vivo scans. Recently, another post-processing technique, known as autofocusing (or autocorrection), has been proposed. In autofocusing, motion is estimated by optimizing an image metric, a measure of image sharpness and quality, while different trial motions are applied to portions of the k-space data. The process is continued until the entire k-space is corrected.
A major drawback of some existing post-processing techniques is the potentially high computational cost. Autofocusing, for example, often requires at least several minutes to correct for two dimensional (2-D) translational motion only. When rotation is also considered, the computation time could be much higher, typically by a factor equal to the number of trial rotations applied. Although a one-dimensional (1-D) successive approach has been proposed to significantly reduce the computation load, this constrained optimization may be sub-optimal when motion is complex. Furthermore, the time required for motion artifact compensation increases as the range of trial motion is increased.